Multi-Objective Optimization Using Evolutionary Algorithms
暫譯: 使用進化演算法的多目標優化

Evolutionary algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.

• Comprehensive coverage of this growing area of research
• Carefully introduces each algorithm with examples and in-depth discussion
• Includes many applications to real-world problems, including engineering design anf scheduling
• Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms

This integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design anf evolutionary computing.

'Deb's book is complete, eminently readable, and the coverage is scholarly and thorough. It is my pleasure and duty to urge you to buy this book, read it, use it and enjoy it' - David E. Goldberg, University of Illinois at Urbana-Champaign, USA

Table of Contents

Foreword.

Preface.

Prologue.

Multi-Objective Optimization.

Classical Methods.

Evolutionary Algorithms.

Non-Elitist Multi-Objective Evolutionary Algorithms.

Elitist Multi-Objective Evolutionary Algorithms.

Constrained Multi-Objective Evolutionary Algorithms.

Salient Issues of Multi-Objective Evolutionary Algorithms.

Applications of Multi-Objective Evolutionary Algorithms.

Epilogue.

References.

Index.